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Random Sampling Dq

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Submitted By sbhatti
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Simple Random Sampling is done when every individual subject in the population has an equal chance of being selected for the sample, without any bias (Explorable). For example, if a researcher wants to represent the population as a whole, they can pick random numbers or names out a hat or use a program to randomly choose names so the information is not biased.

Stratified Sampling is performed by, dividing the population into at least two (or more) groups or sections, which share certain characteristics, called “strata” (Explorable). For example, a researcher who wants to compare the average economic status of different racial groups may use this technique in order to divide the population into groups based on race and ethnicities and then compare the whole average from each ethnic group.

Cluster Sampling is done by dividing the population into separate sections or “clusters” and then picks a cluster randomly and chooses all the members from those clusters for the sample (Explorable). For example, using a geographical cluster, in order to look at the academic performance of students. The researcher can divide Nassau County in Long Island into clusters based on the towns. Then, randomly select a certain number of these clusters or towns and include all the students from those clusters to be part of the sample.

Systematic Sampling is performed by, using and selecting a point at which to begin and then selecting every x number after that point (Explorable). For example, a researcher will choose a starting point within the population, such as a person that walks into a store. Then, using a systematic integer, they will choose every nth number, such as every 3rd person that walks in after the starting person will allow them to get their sample.

Convenience Sampling is done by, collecting data and results that are the easiest to collect (Explorable). For...

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